Conceptual structure: Adults
Two or our three methods for determining how many factors to retain (minimizing BIC and Weisman et al.’s factor retention criteria) suggested retaining three factors. (Parallel analysis suggested retaining four factors; see SOM.)
After rotation, the first factor corresponded primarily to capacities for self- and other-relevant emotions—a suite of capacities that we (following Weisman et al.) will refer to as HEART. It was the dominant factor for such items as feel happy, feel joy, feel proud, and feel sad, and accounted for 37% of the shared variance in the rotated three-factor solution.
The second factor corresponded primarily to physiological sensations related to biological needs—a suite of capacities that we (following Weisman et al.) will refer to as BODY. It was the dominant factor for such items as feel pain, feel scared, feel tired, and get hungry, and accounted for 37% of the shared variance in the rotated three-factor solution.
The third factor corresponded primarily to perceptual-cognitive abilities to detect and use information about the environment—a suite of capacities that we (following Weisman et al.) will refer to as MIND. It was the dominant factor for such items as figure out how to do things, make choices, recognize somebody else, and sense whether something is close by or far away, and accounted for 25% of the shared variance in the rotated three-factor solution.
See Figure 1 for all factor loadings. (Note that for the sake of consistency across studies and comparison with Weisman et al.’s work, we have plotted these factors in the same order for all studies: BODY, HEART, and MIND.)
In sum, as Weisman et al.‘s original studies, a three-factor structure emerged from adults’ mental capacity attributions, characterized by a distinction between body, heart, and mind. This suggests that our child-friendly paradigm was valid: Using reworded items and a three-point response scale elicited the same intuitive ontology of mental life, among adults, that has been revealed by more complex, “adult-friendly” experimental paradigms.
Conceptual structure: Children (7-9y)
Our three methods for determining how many factors to retain all suggested retaining three factors.
After rotation, the first factor corresponded primarily to social-emotional abilities. An analysis of factor congruence confirmed that this factor was most similar to adults’ HEART factor (cosine similarity with HEART: 0.97; with BODY: 0.41; with MIND: 0.43). It was the dominant factor for such items as feel happy, feel joy, feel proud, and get hurt feelings, and accounted for 50% of the shared variance in the rotated three-factor solution.
The second factor corresponded primarily to physiological sensations. An analysis of factor congruence confirmed that this factor was most similar to adults’ BODY factor (cosine similarity with BODY: 0.91; with HEART: 0.26; with MIND: 0.03). It was the dominant factor for such items as feel pain, feel scared, get hungry, and smell things, and accounted for 30% of the shared variance in the rotated three-factor solution.
The third factor corresponded primarily to perceptual-cognitive abilities. An analysis of factor congruence confirmed that this factor was most similar to adults’ MIND factor (cosine similarity with MIND: 0.94; with HEART: 0.35; with BODY: 0.01). It was the dominant factor for such items as be aware of itself, be aware of things, figure out how to do things, and sense whether something is close by or far away, and accounted for 20% of the shared variance in the rotated three-factor solution. (See Figure 1 for all factor loadings.)
In sum, like adults, children’s mental capacity attributions were dominated by a three-way distinction between physiological, social-emotional, and perceptual-cognitive abilities—i.e., body, heart, and mind.

Attributions of mental life: children vs. adults
The factor analyses of correlations among mental capacity attributions just described shed light on one aspect of children’s concepts of mental life: its ontological structure. The design of our study also allows us to look at a second aspect of conceptual development in this domain: children’s application of this concept, i.e., which mental capacities they tend to attribute or not to attribute to the different target characters. How might children’s attributions of capacities related to BODY, HEART, and MIND vary across middle childhood (7-9y), and how do they compare to the mental capacity attributions of adults?
We approached this question from several angles.
First, we projected children’s responses into the factor space defined by adults (standardized in terms of adults’ responses), and examined factor scores by age group (using the correlation-preserving method articulated by ten Berge, Krijnen, Wansbeek, & Shapiro, 1999, as implemented in the “psych” package for R, Revelle, 2018). This yielded three scores for each participant, corresponding, in principle, to holistic judgments of the social-emotional, physiological, and perceptual-cognitive abilities of the target character the participant evaluated. (Note that each of these three scores takes into account factor loadings for all 40 mental capacities, as shown in Figure 1.)
This allowed us to examine the effects of age group (adult, child), character (beetle, robot), and factor (BODY, HEART, MIND) on these scores via mixed effects Bayesian regression. See Table 1 for the results of a maximal model and Figure 2 for scores by age group, age (for children), factor, and character.
Table 1: Fixed effects from a mixed-effects Bayesian regression model predicting factor scores in Study 1 by character (beetle, robot), factor (BODY, HEART, MIND), and age group (adults, children). The model used the formula 'factor score ~ factor * age group * character + (1 | subject)' and was implemented in the 'brms' package for R (Bürkner, 2017). All variables were effect-coded. Asterisks mark parameters whose 95% credible interval does not include 0.
| Parameter |
b |
Error |
95% CI |
|
| (Intercept) |
0.30 |
0.03 |
[ 0.25, 0.36] |
* |
| character (robot vs. grand mean) |
-0.21 |
0.03 |
[-0.26, -0.15] |
* |
| HEART (vs. grand mean) |
0.81 |
0.04 |
[ 0.73, 0.89] |
* |
| MIND (vs. grand mean) |
-0.56 |
0.04 |
[-0.64, -0.48] |
* |
| age group (children vs. grand mean) |
0.30 |
0.03 |
[ 0.24, 0.35] |
* |
| character × HEART |
0.24 |
0.04 |
[ 0.16, 0.32] |
* |
| character × MIND |
0.38 |
0.04 |
[ 0.30, 0.46] |
* |
| character × age group |
0.04 |
0.03 |
[-0.01, 0.10] |
|
| HEART × age group |
0.82 |
0.04 |
[ 0.74, 0.90] |
* |
| MIND × age group |
-0.55 |
0.04 |
[-0.63, -0.47] |
* |
| character × HEART × age group |
-0.02 |
0.04 |
[-0.10, 0.06] |
|
| character × MIND × age group |
-0.04 |
0.04 |
[-0.11, 0.04] |
|
Collapsing across age groups and factors, factor scores suggest that participants generally attributed fewer mental capacities to the robot than the beetle (b = -0.21, 95% credible interval: [-0.26, -0.15]). However, this appears to be entirely due to the huge discrepancy between characters in the BODY domain; the difference between characters was reduced to nothing in the HEART domain (b = 0.24, 95% credible interval: [0.16, 0.32]), and reversed in the perceptual-cognitive domain (b = 0.38, 95% credible interval: [0.30, 0.46]). Collapsing across target characters, children tended to attribute more mental capacities adults (b = 0.30, 95% credible interval: [0.24, 0.35]), but this was driven primarily by the social-emotional domain (b = 0.82, 95% credible interval: [0.74, 0.90]), and was reversed in the perceptual-cognitive domain (b = -0.55, 95% credible interval: [-0.63, -0.47]).

A visual inspection of Figure 2 clarifies these findings. Attributions in the BODY and MIND domains were rather similar for children and adults: Both children and adults marked a clear difference between the robot and the beetle in the physiological sensations of the BODY (left), in line with the animate–inanimate distinction; and both age groups credited the robot with slightly greater perceptual-cognitive skills (MIND) than the beetle (right). In contrast, in the HEART domain (center) both the beetle and the robot received rather low scores among adults, but very high scores among children.
The raw data further supporst these observations. (See Figure 3 for raw counts of no, kinda, and yes responses for all items, grouped by factor, character, and age group.) For example, consider hunger (the first capacity under BODY): Across age groups, nearly every participant said that a beetle could get hungry, while few participants (with the exception of some children) said that a robot could. Likewise, for mathematical computations (the last capacity under MIND), virtually no participants said that a beetle was capable of doing math, while the vast majority of both adults and children said that a robot was. But for social-emotional abilities, like feeling proud, feeling joy, and feeling sad (the first three capacities under HEART), far more children than adults endorsed these capacities for beetles and robots. (See SOM for an analysis, parallel to the regression analyses here, of the proportion of the top-loading mental capacities for each factor that were endorsed by participants of different ages.)
Using alpha for a discrete variable is not advised.


Of course, our sample of “children” included participants as young as 7 years and up to nearly 10 years of age. How did attributions vary across this age range? A visual inspection of Figure 2 suggests that, across all three domains (BODY, HEART, and MIND), children’s mental capacity attributions to beetles and robots appeared to become more adult-like with age—but while the oldest children were indistinguishable from adults in the BODY and MIND domains, even the oldest children appear to have attributed more social-emotional abilities to these entities than most adults did.
A separate regression on children’s factor scores alone confirmed that, while collapsing across factors and target characters children’s overall mental capacity attributions did not vary with age (b = -0.08, 95% credible interval: [-0.18, 0.03]), relative to the grand mean attributions of HEART decreased with age (b = -0.41, 95% credible interval: [-0.56, -0.27]) and attributions of MIND increased with age (b = 0.35, 95% credible interval: [0.21, 0.50]). (See Table 2 for the full results of this model.)
Table 2: Fixed effects from a mixed-effects Bayesian regression model predicting factor scores in Study 1 by character (beetle, robot), factor (BODY, HEART, MIND), and exact age group (for children only). The model used the formula 'factor score ~ factor * age * character + (1 | subject)' and was implemented in the 'brms' package for R (Bürkner, 2017). All categorical variables were effect-coded, and age was mean-centered. Asterisks mark parameters whose 95% credible interval does not include 0.
| Parameter |
b |
Error |
95% CI |
|
| (Intercept) |
0.60 |
0.04 |
[ 0.51, 0.69] |
* |
| character (robot vs. grand mean) |
-0.16 |
0.04 |
[-0.25, -0.08] |
* |
| HEART (vs. grand mean) |
1.63 |
0.06 |
[ 1.50, 1.75] |
* |
| MIND (vs. grand mean) |
-1.10 |
0.06 |
[-1.22, -0.98] |
* |
| age (mean-centered) |
-0.08 |
0.05 |
[-0.18, 0.03] |
|
| character × HEART |
0.21 |
0.06 |
[ 0.09, 0.33] |
* |
| character × MIND |
0.35 |
0.06 |
[ 0.23, 0.47] |
* |
| character × age |
-0.09 |
0.05 |
[-0.20, 0.01] |
|
| HEART × age |
-0.41 |
0.08 |
[-0.56, -0.27] |
* |
| MIND × age |
0.35 |
0.07 |
[ 0.21, 0.50] |
* |
| character × HEART × age |
-0.08 |
0.07 |
[-0.23, 0.06] |
|
| character × MIND × age |
0.06 |
0.08 |
[-0.09, 0.21] |
|
Taken together, these analyses converge to suggest only minor differences between children and adults in their attributions of BODY and MIND to beetles and robots—but a major difference in HEART: Relative to adults, children tended to credit both beetles and robots with much greater social-emotional abilities.